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About

About

I graduated in Applied Sciences (Electricity) from the University of Kinshasa, Kinshasa, Democratic Republic of the Congo, in 1998, and received the M.Sc. and Ph.D. degrees in Electrical Engineering from the Politecnico di Torino, Turin, Italy, in 2004 and 2008, respectively.

For 15 months, I collaborated as a Research Assistant on a project co-funded by Regione Piemonte and Politecnico di Torino – Dipartimento di Ingegneria Elettrica (PVENAS - A procedure based on experimental testing and meteo-database processing to assess the yearly energy production of grid-connected photovoltaic systems) on assessment of solar plant performances.

Since July 2009, I am working as a Senior Research with the Centre of Power and Energy.

My expertise include:

  • Distribution systems modelling and analysis
  • Distributed generation applications,
  • Electricity customer classification,
  • Load and generation forecasting,
  • Renewable energy source integration (namely, wind energy and photovoltaic systems),
  • Operation and planning optimization for Distribution Systems considering uncertainty and flexibility (including Micro Grids and Smart Grids)
  • Tool development

Interest
Topics
Details

Details

  • Name

    Jean Sumaili
  • Cluster

    Power and Energy
  • Role

    Affiliated Researcher
  • Since

    17th July 2009
  • Nationality

    República Democrática do Congo
  • Centre

    Power and Energy Systems
  • Contacts

    +351222094230
    jean.sumaili@inesctec.pt
002
Publications

2018

The challenges of estimating the impact of distributed energy resources flexibility on the TSO/DSO boundary node operating points

Authors
Silva, J; Sumaili, J; Bessa, RJ; Seca, L; Matos, M; Miranda, V;

Publication
Computers and Operations Research

Abstract
The increasing penetration of renewable energy sources characterized by a high degree of variability and uncertainty is a complex challenge for network operators that are obligated to ensure their connection while keeping the quality and security of supply. In order to deal with this variable behavior and forecast uncertainty, the distribution networks are equipped with flexible distributed energy resources capable of adjusting their operating point to avoid technical issues (voltage problems, congestion, etc.). Within this paradigm, the flexibility that, in fact, can be provided by such resources, needs to be estimated/forecasted up to the transmission network node (primary substation) and requires new tools for TSO/DSO coordination. This paper addresses this topic by developing a methodology capable of finding the flexibility area while taking into account the technical grid constraints. The proposed approach is based on the formulation of a single optimization problem which is run several times, according with the expected precision for the flexibility area estimation. To each optimization problem run, a different objective function belonging to a family of straight lines is assigned. This allows exploring the active and reactive power flow limits at the TSO/DSO boundary nodes - which define the flexibility area. The effectiveness of the proposed model has been evaluated on two test networks and the results suggest a step forward in the TSO/DSO coordination field. Nevertheless, further investigations to study the effect of assets with discrete control nature (e.g., on load tap changers - OLTC, capacitor banks) on the occurrence of disjoint flexibility areas should be carried. © 2017 Elsevier Ltd.

2017

Assessing the Impact of Demand Flexibility on Distribution Network Operation

Authors
Tavares, BD; Sumaili, J; Soares, FJ; Madureira, AG; Ferreira, R;

Publication
2017 IEEE MANCHESTER POWERTECH

Abstract
This paper presents a study about the influence of Distributed Energy Resources' (DER) flexibility on the operation of a Medium Voltage (MV) network, in a Smart Grid (SG) environment. An AC multi-temporal Optimal Power Flow (OPF) tool was developed and used to simulate the impact of the DER flexibility (including storage devices, EVs, controllable loads and micro-generation) in distribution network operation. Some simulations are presented, demonstrating the impact that DER flexibility can have on solving operation problems namely in terms of branch loading and voltage limits.

2017

Mitigation in the Very Short-term of Risk from Wind Ramps with Unforeseen Severity

Authors
Pinto, M; Miranda, V; Saavedra, O; Carvalho, L; Sumaili, J;

Publication
JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS

Abstract
This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

2017

Mean shift densification of scarce data sets in short-term electric power load forecasting for special days

Authors
Rego, L; Sumaili, J; Miranda, V; Frances, C; Silva, M; Santana, A;

Publication
ELECTRICAL ENGINEERING

Abstract
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.

2016

Assessing DER flexibility in a German distribution network for different scenarios and degrees of controllability

Authors
Silva, A; Carvalho, L; Bessa, R; Sumaili, J; Seca, L; Schaarschmidt, G; Silva, J; Matos, M; Hermes, R;

Publication
IET Conference Publications

Abstract
This paper evaluates the flexibility provided by distributed energy resources (DER) in a real electricity distribution network in Germany. Using the Interval Constrained Power Flow (ICPF) tool, the maximum range of flexibility available at the primary substation was obtained for different operation scenarios. Three test cases were simulated, differing mainly in the considered level of renewable energy sources (RES) production. For each test case, the obtained results enabled the construction of flexibility areas that define, for a given operating point, the limits of feasible values for the active and reactive power that can be exchanged between the TSO and the DSO. Furthermore, the tool can also be used to evaluate the contribution from each type of DER to the overall distribution network flexibility.